Climate change implications for the glaciers of the Hindu Kush, … · 2020. 6. 23. · data (Kääb et al., 2012) revealed a complex pattern in mass balance (Table 1), with the Karakoram

The Cryosphere, 8, 941–958, 2014www.the-cryosphere.net/8/941/2014/doi:10.5194/tc-8-941-2014© Author(s) 2014. CC Attribution 3.0 License.

Climate change implications for the glaciers of the Hindu Kush,Karakoram and Himalayan regionA. J. Wiltshire

Met Office Hadley Centre, Exeter, Devon, EX1 3PB, UK

Correspondence to:A. J. Wiltshire ([email protected])

Received: 13 June 2013 – Published in The Cryosphere Discuss.: 23 July 2013Revised: 21 March 2014 – Accepted: 27 March 2014 – Published: 22 May 2014

Abstract. The Hindu Kush, Karakoram, and Himalaya(HKH) region has a negative average glacial mass balancefor the present day despite anomalous possible gains in theKarakoram. However, changes in climate over the 21st cen-tury may influence the mass balance across the HKH. Thisstudy uses regional climate modelling to analyse the im-plications of unmitigated climate change on precipitation,snowfall, air temperature and accumulated positive degreedays for the Hindu Kush (HK), Karakoram (KK), Jammu–Kashmir (JK), Himachal Pradesh and West Nepal regions(HP), and East Nepal and Bhutan (NB). The analysis focuseson the climate drivers of change rather than the glaciologicalresponse. Presented is a complex regional pattern of climatechange, with a possible increase in snowfall over the westernHKH and decreases in the east. Accumulated degree days areless spatially variable than precipitation and show an increasein potential ablation in all regions combined with increasesin the length of the seasonal melt period. From the pro-jected change in regional climate the possible implicationsfor future glacier mass balance are inferred. Overall, withinthe modelling framework used here the eastern Himalayanglaciers (Nepal–Bhutan) are the most vulnerable to climatechange due to the decreased snowfall and increased ablationassociated with warming. The eastern glaciers are thereforeprojected to decline over the 21st Century despite increas-ing precipitation. The western glaciers (Hindu Kush, Karako-ram) are expected to decline at a slower rate over the 21stcentury in response to unmitigated climate compared to theglaciers of the east. Importantly, regional climate change ishighly uncertain, especially in important cryospheric driverssuch as snowfall timing and amounts, which are poorly con-strained by observations.

Data are available from the author on request.

1 Introduction

The high mountains of South Asia covering the Hindu Kush,Karakoram and Himalaya (HKH) belt have been described asthe “Water Tower of Asia” (Viviroli et al., 2007; Immerzeelet al., 2010) due to their important role in feeding the majorrivers of South Asia. Water resources are considered vulner-able in the region due to the important role of snow and icein the hydrology of the region (Barnett et al., 2005). Seasonalstorage of water in snow and ice acts to delay the timing ofrunoff (Kaser et al., 2010; Immerzeel et al., 2010; Sideriuset al., 2013; Schaner et al., 2012). The retreat and loss ofglaciers may therefore have a significant impact on the quan-tity and timing of water availability as is currently observedin parts of the tropical Andes (Rabatel et al., 2013).

The vulnerability of water resources to climate changeis further enhanced due to the coincidence of a large andgrowing population. For example, the Indus originates in theTibetan plateau and carries melt water from the glaciatedKarakoram and Jammu–Kashmir region to a highly popu-lated region. The flow component arising from glacial dis-charge is highly uncertain and estimates vary from 40 % forthe upper Indus (Immerzeel et al., 2010) to as low as 2 %in one of the glaciated sub-catchments (Jeelani et al., 2012).However, seasonally, there is potentially a very large pop-ulation reliant on glacier discharge (Schaner et al., 2012).Regionally over 1.4 billion people depend on water fromthe Indus, Ganges, Brahmaputra, Yangtze, and Yellow rivers(Moors et al., 2011; Immerzeel et al., 2010) which arisein the HKH and Tibetan plateau (not considered here), al-though the influence of glacier discharge will decrease withdistance downstream and river flow will become more influ-enced by precipitation variability (Kaser et al., 2010). There

Published by Copernicus Publications on behalf of the European Geosciences Union.

942 A. J. Wiltshire: Climate change implications for Himalayan glaciers

is correspondingly a need to better quantify the potential im-pacts of climate change on regional water resources.

The HKH have attracted controversy over reported ratesof glacier ice loss. The IPCC WGII AR4 (Cruz et al., 2007)reported an erroneous statement from WWF (2005) thatglaciers may disappear by 2035 (Cogley et al., 2010) withsignificant implications for water resources. More recentwork has shown that in some parts of the region glaciers havebeen increasing in mass over the last decade – the “Karako-ram anomaly” (Hewitt, 2005; Gardelle et al., 2013). Thereare few direct observations from the region (Bolch et al.,2012) but a number of recent studies have demonstrated thatthe glaciers of the HKH have experienced negative mass bal-ance overall in the last decade (1999 to 2011) (Jacob et al.,2012; Kääb et al., 2012; Gardelle et al., 2013; Gardner et al.,2013). A study by Fujita and Nuimura (2011) looking at threebenchmark glaciers found evidence of rapid glacier wastagesince the 1970s. The humid lower glaciers have a more neg-ative mass balance than the arid high-elevation benchmarkglacier, implying that the lower glaciers are more sensitiveto change. Observational data suggest that the change inglacier state is spatially heterogeneous across the region (Fu-jita and Nuimura, 2011) potentially due to climate (Fowlerand Archer, 2006) and glaciological and locational factors,such as elevation and glacier debris cover (Scherler et al.,2011). The Karakoram anomaly relates to a sub-region ofthe HKH in which glaciers may have increased mass or re-mained relatively stable over the last decade (Gardelle etal., 2012; Hewitt, 2005, 2011; Kääb et al., 2012; Gardelleet al., 2013) in contrast to other parts of the HKH. Evi-dence for the Karakoram anomaly comes from other sources– for instance Fowler and Archer (2006) found evidenceof a cooling trend between 1961 and 2000 consistent withglacier thickening. Copland et al. (2011) found evidence ofincreased glacier surging in the 1990s compared with previ-ous decades consistent with increased precipitation. The onlyin situ data source for the Karakoram indicates an averagebudget of−0.51 m year−1 w.e. for Siachen Glacier (1986 to1991) (Bhutiyani, 1999). It should be noted that all these re-sults are based on limited observations from which regionaltrends are difficult to infer. A study using satellite altimetrydata (Kääb et al., 2012) revealed a complex pattern in massbalance (Table 1), with the Karakoram glaciers thinning byonly a few centimetres a year with greater rates of loss inthe Hindu Kush and the central and eastern Himalayas. Thegreatest rates of loss were found in the Jammu–Kashmir re-gion. Recent updates are broadly in agreement but found thatthe Karakoram anomaly extended further northwest into thePashmir region, and higher rates of mass loss in the easternHimalayas (Gardelle et al., 2013; Gardner et al., 2013).

Although some evidence points to climate change as apotential cause of the Karakoram anomaly, other explana-tions include the existence of large areas of debris cover onKarakoram glaciers which may reduce sensitivity to change(Scherler et al., 2011) and thus contribute to the spatial het-

erogeneity, although Kääb et al. (2012) and others found noevidence for this. Snow avalanches may also be an importantmass balance component in the Karakoram due to the steepand rugged terrain (Hewitt, 2011).

The spatial heterogeneity in mass balance is thereforelikely to be partly linked to spatial variation in climatechange and variability, the short length of observational massbalance data available and the variation in glaciological con-ditions. Other non-climatic drivers such as deposition of dustand soot may also play a role, with some evidence that thismay already be having an effect on some Tibetan glaciers(Xu et al., 2009).

There are relatively few studies that aim to make a compre-hensive assessment of the future glacier state under climatechange. A global study by Radic and Hock (2010) focused onsea-level rise found an average ice volume loss of 10± 16 %between 2000 and 2100 under the SRES A1B scenario. Thelarge uncertainty in their result spanning a possible increaseto decrease is related to the future regional climate uncer-tainty (Cruz et al., 2007; Meehl et al., 2007). The uncertaintyis further exacerbated by the complex orography of the HKHwhich is not captured in coarse-scale global climate models.However, the possibility of a future increase in glacier vol-ume as found by Radic and Hock (2010) highlights the im-portance of changing precipitation patterns and the potentialfor increasing snowfall under global warming. This in turnmay offset any increased ablation due to warming, possiblyleading to glacier thickening and advance in some regionsunder some scenarios.

This study aims to increase understanding of the potentialimpacts of climate change on the glaciers of the Himalayasand Hindu Kush. In particular the issue of the relative roles ofchanging accumulation and ablation in the context of climateuncertainty are addressed. A regional climate model (RCM)is employed to downscale global GCM climate simulationsunder the SRES A1B scenario (Nakicenovic et al., 2006), andproduce a best estimate of the present day by down-scalingERA-Interim reanalysis (Dee et al., 2011). The simulationsare performed at 25 km in an attempt to capture the complexrole of orography whilst retaining computationally efficacy.Two GCM simulations are used from models shown to cap-ture the dynamics of the monsoon; the two models are cho-sen to sample the spread in precipitation uncertainty froma possible future increase to a decrease. Due to the generallack of quality observational data across the high mountainsthe ERA-Interim simulation is used as a benchmark againstwhich to evaluate the GCM derived simulations of the future.The application of RCMs to regions of complex topogra-phy remains challenging, partly due to their relatively coarseresolution in comparison to topography. However, the ERA-Interim simulation has been shown to well represent the roleof steep topography on moisture transport fluxes and verti-cal flow for the western Himalayas (Dimri et al., 2013), andthe GCM-driven models have been shown to capture the syn-optic patterns influencing winter precipitation in the region.

The Cryosphere, 8, 941–958, 2014 www.the-cryosphere.net/8/941/2014/

A. J. Wiltshire: Climate change implications for Himalayan glaciers 943

Table 1.Elevation statistics by region as used in the RCM at 25 km resolution and derived from the GMTED2010 high-resolution elevationdata set (in brackets) (Danielson and Gesch, 2011). Also shown are the satellite-derived glacier mass balance 2003–2009 from Kääb etal. (2012).

Mean Maximum Minimum Masselevation elevation elevation balance(m a.s.l.) (m a.s.l.) (m a.s.l.) m w.e. year−1

Hindu Kush 3779 (4067) 4737 (7794) 2645 (2501) −0.2± 0.06Karakoram 4885 (4972) 5835 (8216) 3647 (2502)−0.03± 0.04Jammu–Kashmir 4128 (4293) 5263 (6728) 2519 (2501)−0.55± 0.08Himachal Pradesh, Uttarakhand and West Nepal 4616 (4622) 6142 (7751) 2532 (2502)−0.32± 0.06East Nepal and Bhutan 4734 (4700) 5951 (8642) 2568 (2501)−0.3± 0.09

Overall, the use of regional climate modelling over the In-dian subcontinent has been shown to significantly improvethe regional detail of projected climate change (Lucas-Picheret al., 2011; Mathison et al., 2013; Kumar et al., 2006, 2013).

The simulations are analysed in terms of the possible fu-ture climate drivers of glacier accumulation and ablationacross the HKH in the context of climate uncertainty andspatial heterogeneity. As this study is focused on the climaticdrivers, other factors that may influence the complex glacio-logical response such as debris cover, common within theHimalayas, are not considered. Detection and attribution ofrecent changes in mass balance is not within the scope of thisstudy.

2 Baseline regional climate

The climate of South Asia is characterised by the strongmeridional temperature gradient between the high mountainsof the HKH and the subcontinent of India (Fig. 1). The cli-mate of the HKH is heavily influenced by three circulationsystems; Western Disturbances (WD), the Indian SummerMonsoon (ISM) and the East Asian Monsoon (EAM). Thesethree systems are largely responsible for the patterns of sea-sonal precipitation that can be seen in Fig. 2, with much ofthe precipitation in the region occurring over the extendedsummer monsoon months (May to October). The region has astrong temperature contrast, with the high mountains havingan annual mean temperature below 0◦C, and the lower plainsin excess of 20◦C. Across the Himalayan arc the KarakoramRange is cooler, related to on average higher altitudes, thanthe rest of the region (Table 1, Fig. 1).

The WD bring moisture from the Mediterranean Sea overthe northwestern parts of the Indian subcontinent. The sys-tem is particularly responsible for bringing snowfall duringthe winter and pre-ISM months. The ISM brings moisturefrom the Bay of Bengal, causing intense rainfall over theregion during the summer months, and is partly linked tosnowfall during the summer months towards the eastern endof the Himalayan arc (Fig. 3). The influence of the ISM de-creases strongly further west of approximately∼ 77◦ E in the

Garhwal Himalayas (Bookhagen and Burbank, 2006, 2010),separating the Jammu–Kashmir (JK) region from HimachalPradesh, Uttarakhand and West Nepal (HP). West of this di-vide precipitation is dominated by WD with the ISM havinga diminishing role.

The mountains of the Himalayan arc provide a barrier tothe northward progression of moisture, separating the plainsof South Asia from the relatively dry Tibetan Plateau to thenorth (Fig. 2). Some moisture is able to move north throughthe deep valleys cut by rivers flowing south to join theGanges. The rise of the Himalayan arc through the foothillsto the high Himalayas leads to considerable orographic up-lift and thus considerable precipitation along the orographicbarrier (Dimri et al., 2013).

The combination of the interaction between the circula-tion systems and the Himalayas, as well as elevation and lat-itudinal differences, leads to a strong gradient in precipita-tion from the Hindu Kush (HK) in the West to Nepal–Bhutan(NB) in the East, as well as a strong gradient from the Hi-malayan foreland into the Tibetan Plateau. There is less ofa gradient in total snowfall along the HKH arc but there isa variation in the seasonal timing of snowfall. The regionsfurther west (HK, KK) are dominated by winter accumula-tion with summer snowfall also playing a role further eastin the Nepal–Bhutan region (Fig. 3). The glaciers in the east(NB) are therefore monsoon influenced experiencing somesummer accumulation, whilst those to the west are primar-ily winter accumulating (HK, KK). The timing of potentialmelt as defined by seasonality of air temperature is less spa-tially variable, with all regions demonstrating summer abla-tion. This has important implications for seasonal water stor-age and water resources. The eastern glaciers are monsooninfluenced and experience some additional summer accumu-lation, as well as summer ablation, and therefore do not storewater seasonally to the same extent as the western glaciers(HK, KK). These monsoon-affected summer accumulationglaciers may also be more vulnerable to warming (Fujita,2008a, b) as any increase in ambient temperature is likelyto have a double effect; increasing potential melt as wellas decreasing the likelihood of precipitation falling as snow.This is due to the overall higher summer ambient temperature

www.the-cryosphere.net/8/941/2014/ The Cryosphere, 8, 941–958, 2014


27

794

Figure 1: Annual mean air temperature of the Indian Subcontinent (1992-2007) as simulated by 795

downscaling ERA-Interim reanalysis using the HadRM3 regional climate model compared to CRU 796

observations. The region above 2500 m a.s.l. is indicated. Note that the RCM data are on a rotated 797

pole projection. 798

799

Figure 1. Annual mean air temperature of the Indian Subcontinent (1992–2007) as simulated by downscaling ERA-Interim reanalysis usingthe HadRM3 regional climate model compared to CRU observations. The region above 2500 m a.s.l. is indicated. Note that the RCM dataare on a rotated pole projection.

28

800

801

Figure 2: Precipitation over the Indian Subcontinent (1992-2007) as simulated by downscaling ERA-802

Interim reanalysis using the HadRM3 regional climate model compared against observation data 803

from the Indian Meteorological department (Rajeevan et al., 2008) and Aphrodite (Yatagai and 804

Yasunari, 1994). The 2500 m a.s.l. contour is shown on the left-hand plots. Note that the RCM data 805

are on a rotated pole projection. 806

807

Figure 2. Precipitation over the Indian Subcontinent (1992–2007) as simulated by downscaling ERA-Interim reanalysis using the HadRM3regional climate model compared against observation data from the Indian Meteorological department (Rajeevan et al., 2008) and Aphrodite(Yatagai and Yasunari, 1994). The 2500 m a.s.l. contour is shown on the left-hand plots. Note that the RCM data are on a rotated poleprojection.



compared to the winter. Any warming is therefore likely toincrease the number of days above the critical threshold of0◦C more in the summer than in the winter.

3 Methods

This study employs regional climate modelling of selectedglobal climate projections to simulate the spatial heteroge-neous patterns in meteorology across the HKH that may bedriving some of the variation in mass balance. This studyuses regional climate models (RCMs) to both simulate thepresent-day climatology of the HKH as well as make centen-nial projections of climate change. The aim is not to makedirect future projections of mass balance but to assess pro-jections of the climate drivers of mass balance, namely sim-ulated precipitation, temperature and snowfall. The majorityof the results presented here are therefore in anomaly spaceas multi-decadal means relative to the present day (e.g. re-sults for the 2080s are the mean 2071–2100 climate anoma-lised to 1992–2008). However, it is not possible to work en-tirely in anomaly space in confidence without some evalu-ation of present-day model biases. An overview evaluationof the present-day model biases is therefore included to addconfidence in the future simulated change. Regional climatebiases are currently an unavoidable shortcoming in globalclimate models.

Snowfall is used as a measure of change in the future ac-cumulation of glaciers, and from daily air temperature posi-tive degree days (PDDs) are calculated. PDDs can be linkedto potential melt through degree day factors (Hock, 2003,2005). A PDD is defined as the cumulative sum of daily meantemperatures over 0◦C for a month. Temperature index mod-elling using degree days is thus a simplification of complexprocesses controlling the energy balance at the surface. Thebasis for degree day modelling is the observed close rela-tionship between air temperature and melt (Ohmura, 2001;Hock, 2003). Temperature index models are therefore widelyused and can perform well in comparison with energy bal-ance models at large scales (Hock, 2005).

3.1 Regional climate modelling

The porosity of spatially explicit meteorological observationdata across the HKH region is a significant barrier to under-standing the climate interactions with the HKH cryosphere.For instance, there are few precipitation gauges within theHimalayas (Bookhagen and Burbank, 2010; Yatagai et al.,2009). For this region Akhtar et al. (2008) found a betterperformance with a hydrological model when using RCMdata than when using poor-quality observation data imply-ing greater confidence in the RCM simulated meteorologythan available observational data. Global observation datasets are typically coarse and do not capture the complex to-pography in the region (Kang et al., 2002). For this reason

the HadRM3 RCM (HadRM3 is a regional version of theHadAM3 global atmosphere model (Pope et al., 2000; Gor-don et al., 2000). The version used here is coupled to theMOSES2.2 land surface scheme (Essery et al., 2003) and isemployed to dynamically downscale ERA-Interim reanaly-sis (Dee et al., 2011). This provides a baseline of the presentday climate to assess the climate simulations against. Themeteorological reanalysis provides a consistent estimate ofthe atmospheric state of the recent past, and the downscaledproduct is thus our best estimate of the climate state despiteadditional errors and uncertainty from the reanalysis processand the downscaling itself (Lucas-Picher et al., 2011). Thedownscaled ERA-Interim scenario enables the evaluation ofthe regional climate biases in the climate simulations. Thescenario can also be evaluated in conjunction with availableobservations to assess the ability of the RCM to accuratelysimulate climate over complex terrain. To capture both therole of orography and the large-scale regional circulationthe climate simulations are run at 25 km for the whole In-dian subcontinent (Fig. 1). Although this resolution is fine interms of centennial climate simulations for most regions, inthe Himalayas 25 km is not fine enough to resolve topogra-phy. Other applications of numerical modelling in the highmountains have focused on the shorter weather timescalesand have employed significantly higher resolutions in theorder of kilometres (e.g. Maussion et al., 2011). Associ-ated, explicit modelling of the microclimate over glaciersalso requires an atmospheric modelling setup in the kilo-metre resolution (e.g. Collier et al., 2013; Molg and Kaser,2011). The choice of 25 km is therefore a necessary compro-mise between the desire to sample climate uncertainty in theform of an ensemble, length of simulation, available super-computer time (one centennial simulation consumes approx-imately four months of supercomputer time) and the resolu-tion necessary to appropriately resolve orographic processes.For context, the decision to use 25 as opposed to 50 km in-creased the computational requirement by a factor of 8, andcorrespondingly reduced the number of ensemble memberspossible to sample uncertainties. Additionally, in order tosample future climate uncertainty two global climate model(GCM) simulations are downscaled covering 1960–2100 forthe SRES A1B scenario (Nakicenovic et al., 2006). The twoscenarios are from versions of ECHAM5 (Roeckner et al.,2003) and HadCM3 (Pope et al., 2000; Gordon et al., 2000).The impact of climate change on the ISM is considerablyuncertain in the IPCC AR4 CMIP3 (IPCC, 2007) ensemblewith model responses varying on the sign of the direction ofchange (Christensen et al., 2007). Climate models also showsignificant differences in their ability to simulate the ISM inthe present day (Annamalai et al., 2007). Some of the futureuncertainty may therefore be associated with models with alack of skill in simulating the ISM. Of the CMIP3 ensem-ble only four models were shown to be able to capture thelarge-scale statistics of the ISM under the present climate(Annamalai et al., 2007; Kripalani et al., 2007; Turner and



29

808 Figure 3: Annual snowfall accumulation along the Himalayan belt (left), winter snowfall (December 809

to March) accumulation (centre) and the fraction of annual snowfall occurring during the winter. 810

Data are 1992-2008 averages from ERA-Interim downscaled with the HadRM3 regional climate 811

model. 812

813

Figure 3. Annual snowfall accumulation along the Himalayan belt (left), winter snowfall (December to March) accumulation (centre) andthe fraction of annual snowfall occurring during the winter. Data are 1992–2008 averages from ERA-Interim downscaled with the HadRM3regional climate model.

30

814

Figure 4: Change in precipitation (mm per day) by the 2080s relative to 1961-1990 under the SRES 815

A1B scenario for four selected CMIP3 models and a flux-corrected version of HadCM3. The region 816

above 2500 m a.s.l. is indicated. 817

818

Figure 4. Change in precipitation (mm per day) by the 2080s relative to 1961–1990 under the SRES A1B scenario for four selected CMIP3models and a flux-corrected version of HadCM3. The region above 2500 m a.s.l. is indicated.

Annamalai, 2012). The uncertainty in the monsoon precipi-tation response to global warming is related to two key op-posing processes responding to global warming: the thermo-dynamic forcing whereby atmospheric moisture content in-creases, and the dynamical weakening of the monsoon dueto weakening of circulation patterns (Ueda et al., 2006).

The standard CMIP3 HadCM3 model was not able to cap-ture the monsoon dynamics. However, a version of HadCM3using flux correction performed better. The flux adjustmentof HadCM3 improved the mean state of the tropical Pacificimproving the simulation of ENSO, this in turned improvedthe monsoon–ENSO relationship and the overall simulationof the ISM (Turner et al., 2005). There are therefore five pos-sible GCMs available that are considered able to capture themonsoon dynamics (Fig. 4) critical to appropriately simu-

lating the impact of climate change on the high-mountaincryosphere of South Asia. In this study the ECHAM5 andflux-corrected HadCM3 simulations are used, chosen to sam-ple uncertainty in the sign of projected precipitation change.ECHAM5 simulates a decrease in precipitation for the HKHregion and HadCM3 an increase.

3.2 Sub-regional definitions and baseline glacier state

To understand the spatial variation in climate drivers and inglacier mass balance, distinct sub-regions across the HKHare defined to capture the variation in climate and ob-served glaciological patterns. Following the classification ofKääb et al. (2012) the five sub-regions are defined as (Ta-ble 1 and Fig. 5): Hindu Kush (HK), Karakoram (KK),



Jammu–Kashmir (JK), Himachal Pradesh, Uttarakhand andWest Nepal (HP) and East Nepal and Bhutan (NB). In fur-ther analysis the sub-regions above 2500 m a.s.l. are used toform area averages. The majority of the glaciers in the studyregion are covered by the extended World Glacier Inventory(WGI) (Cogley, 2010) whilst the Randolph (RGI) (Arendt etal., 2012) data set provides a complete coverage. The use ofregional definitions consistent with Kääb et al. (2012) allowsthe use of the mass balance observations as a baseline fromwhich to assess the possible impacts of climate change (Ta-ble 1).

One of the significant challenges in using RCMs to analysethe implications of climate change on glacier mass balance isthe ability to resolve glaciers which are typically at the sub-grid scale. The RCM simulates meteorological fields at theelevation of the grid box mean. A second challenge is param-eterising the sub-grid variation in topography effect on me-teorology. The model has too coarse a resolution to resolvemesoscale processes such as convection and small-scale val-ley flows which affect motions of vertical flow important tosnow rainfall discretisation and instead rely on parameterisa-tions. Figure 5 shows the topography in the GMTED2010high resolution (30 arcsec) (Danielson and Gesch, 2011)alongside the elevation data as used in the RCM (processed10 min data, US Navy, 1980). A comparison of the area–altitude distribution from the two fields reveals that the RCMcaptures the elevation distribution across the HKH domainin the more up-to-date higher resolution product; howeverat elevations above 4500 m the RCM data is biased a fewhundred metres high but low compared to elevations in ex-cess of 5500 m. This bias is introduced by the smoothingof steeply varying topography in this region. Further analy-sis shows that the standard deviation of sub-grid topographycan reach up to 1500 m in some grid boxes where the to-pography rises steeply. The impact of topography smoothingis further affected by the likelihood of glaciers to be biasedhigh compared to the grid box mean elevation. An analysis ofthe GLIMS glacier topography inventory (Raup et al., 2007;GLIMS, 2005) reveals that for some parts of the region themean glacier elevation can be up to 2500 m above the RCMgrid box mean. In a few cases the grid box mean is up to750 m above the mean glacier elevation. The GLIMS data(inventory version glims_db_20130813) were used for thispurpose as topographic information is available as part of theinventory. However, GLIMS elevation data represent only asubset of all the glaciers in the RGI with gaps in most re-gions and virtually no altitude information for KK and HK.An important caveat to the results presented here is that theyare based on grid box mean elevations which have an inher-ent elevation bias to the glaciers of the HKH. This limitationand inherent climate biases in regional climate modelling isthe reason why this study focuses on the climate drivers ofchange rather than attempting to simulate mass balance di-rectly.

4 Results

4.1 Simulations of the recent past

In this section the ability of the RCM simulations to capturethe climate of the recent past is evaluated. The ERA-Interimsimulation captures the large-scale features in temperatureand precipitation (Figs. 1 and 2). Across the HKH regionthere is a cold bias relative to the CRU temperature data set.The annual and monsoon precipitation is well captured inthe RCM compared with IMD (Rajeevan et al., 2008) andAphrodite (Yatagai et al., 2009) observations (Fig. 2). Thefraction of annual precipitation that falls during the monsoonis also reasonably simulated. A close analysis of precipita-tion over the HKH suggests that the RCM has a wet bias athigh elevations. However, there is a significant lack of obser-vations in particular at high elevations and the observed biasmay be enhanced due to the lack of gauge under catch cor-rections in the Aphrodite data. PDDs and snowfall are partic-ularly sensitive to biases in temperature and precipitation andare difficult to evaluate in the absence of high-resolution spa-tially explicit observations. The climatology for these vari-ables over the key regions for the recent past is shown inFig. 6.

Across all regions temperatures peak above 0◦C duringthe summer with strong within-region variation associatedwith the large variation in altitude. The KK is significantlycolder in the winter, with NB the warmest due to latitu-dinal differences despite similar elevations (Table 1). NBalso shows the least seasonal variation in temperature. TheGCM driven runs show small biases in temperature relativeto ERA-Interim, suggesting that any systematic bias is intro-duced by the downscaling rather than the forcing. However,small differences in temperature can have a large influenceon the number of PDD, with HadCM3 simulating a factor ofa third more in the summer compared to ERA-Interim. Smallbiases are seen in the other regions, with the lower HK andJK regions having higher degree days than the other regions.NB, HP and JK also show small numbers of positive degreedays in the winter despite mean winter temperatures of−15to −20◦C.

The timing and amount of precipitation varies stronglywith region. The highest precipitation occurs in NB duringthe summer monsoon. Further west, where WD prevail, peakprecipitation falls in the spring. The more central JK region isaffected by both western disturbances and the monsoon witha double peak in precipitation. The GCMs show a similarpattern to ERA-Interim with ECHAM5 showing some biasin the timing and magnitude of peak monsoon precipitation.

Accurately simulating snow/rain differentiation in RCMs,particularly in regions of complex terrain, is significantlychallenging and the models are likely to be significantly lim-ited in their ability to capture this. Of the climate drivers pre-sented here snowfall amounts are likely to be most uncer-tain. In KK, HK and JK snowfall mainly occurs during the



31

819

Figure 5: Location map of the Hindu-Kush, Karakoram Himalaya region showing the location of 820

glaciers from the extended World Glacier Inventory (Cogley, 2010) (left), and the glaciated fraction 821

aggregated to half degree from the Randolph glacier inventory (Arendt et al., 2012) (right). Also 822

shown is the area-altitude distribution as used in the RCM and from the high resolution GMTED10 823

(Danielson and Gesch, 2011) product. Highlighted are the Hindu Kush (HK), Karakoram (KK), Jammu-824

Kashmir (JK), Himachal Pradesh, Uttarakhand and West Nepal (HP) and East Nepal and Bhutan (NB) 825

sub-regions used in this study. 826

827

Figure 5. Location map of the Hindu Kush, Karakoram and Himalaya region showing the location of glaciers from the extended WorldGlacier Inventory (Cogley, 2010) (left), and the glaciated fraction aggregated to half degree from the Randolph glacier inventory (Arendt etal., 2012) (right). Also shown is the area–altitude distribution as used in the RCM and from the high-resolution GMTED10 (Danielson andGesch, 2011) product. Highlighted are the Hindu Kush (HK), Karakoram (KK), Jammu–Kashmir (JK), Himachal Pradesh, Uttarakhand andWest Nepal (HP) and East Nepal and Bhutan (NB) sub-regions used in this study.

winter months associated with western disturbances (Ridleyet al., 2013). However, the more central and eastern HP andNB show significant summer snowfall, with strong disagree-ment between models. Despite the lack of long-term observa-tions a measurement campaign along a elevation crossing theAnnapurna range found little summertime snowfall (Putko-nen, 2004). The maximum observed snow accumulation of1000 mm of snow water equivalent (SWE) at high elevationsis comparable to the∼ 900 mm SWE snowfall simulated inHP, implying too much summer snowfall and not enough dur-ing the winter. Amongst the models there is some disagree-ment in the volume of snowfall occurring during the winter.The timing and volume of snowfall is likely to be very sensi-tive to the cold and wet biases in the model.

Although the simulations show variation in climate be-tween the sub-regions this doesn’t allow an inference on theclimate role in observed trends in mass balance, rather it sug-gests that the glaciers of the region most likely have differ-ing characteristics associated with varying climatic regime.To infer the role of climate change over the late 20th andearly 21st century requires a different approach to attributethe detected changes in mass balance to a shift in climate. In-stead, the projected changes over the 21st century as driversof cryospheric change are analysed.

4.2 Projected changes in HKH climate

Over the 21st century under the SRES A1B emissions sce-nario the HKH region is projected to warm by 4–5.5◦C rel-ative to 1971–2000 (Fig. 7). This is a faster rate of warmingthan the global and regional rates of increase. This is partlydue to the land warming faster than the oceans and reduc-tions in the simulated seasonal and perennial snow cover de-creasing the surface albedo. The ECHAM5 simulation hasthe higher rate of warming, which may be linked with thegreater reductions in snowfall and seasonal snow cover inthis simulation. In both simulations the majority of the HKHremains below freezing but there is an increase in the heightof the annual mean zero degree isotherm. For all regions andboth models the rate of warming is greatest during the wintermonths. However, the seasonal warming differs between themodels with ECHAM5 showing uniform warming for KK,HK and JK of around 5◦C by the 2080s (30 yr mean cen-tred on 2085) whilst HadCM3 gives 3◦C in spring but nearly6◦C in the winters (Figs. 8 and 9). The trend in temperaturechange is similar between models and regions until the 2050sat which point ECHAM5 has a stronger rate of warming thanHadCM3 (Fig. 10).

Although rapid warming is projected in the HKH thisdoesn’t necessarily translate into higher rates of ablation. Theindex of ablation used here is positive degree days above zero(PDD). Changes in PDD are highest where this is a seasonal



32

828

829

Figure 6: 30-year mean climatologies of air temperature, degree days, precipitation and snowfall for 830

the five sub-regions defined in this study from the downscaled ERA-Interim (green), HadCM3 (black) 831

and ECHAM5 (yellow) simulations for 1992-2008. The regions are Hindu Kush (HK), Karakoram (KK), 832

Jammu-Kashmir (JK), Himachal Pradesh, Uttarakhand and West Nepal (HP) and East Nepal and 833

Bhutan (NB). 834

835

Figure 6. Thirty-year mean climatologies of air temperature, PDDs, precipitation and snowfall for the five sub-regions defined in this studyfrom the downscaled ERA-Interim (green), HadCM3 (black) and ECHAM5 (yellow) simulations for 1992–2008. The regions are HinduKush (HK), Karakoram (KK), Jammu–Kashmir (JK), Himachal Pradesh, Uttarakhand and West Nepal (HP) and East Nepal and Bhutan(NB).

transition in air temperature from below freezing to above.Relatively very cold regions and very hot regions will showsmall changes in PDD. This can be seen in Fig. 7, whichshows that the greatest increases are in the mountains, withsmall changes across the Indo-Gangetic plain. The highestand coldest parts of the HKH show small changes in PDD.The biggest annual increase in PDD occurs in NB and HPassociated with a broadening of the potential melt seasonwith significant increases during the winter. This broaden-ing is linked to the winters being warmer than the rest of theHKH, so that any climate warming leads to a larger increasein PDD. By the 2080s the mean temperature difference be-tween the ECHAM5 and HadCM3 simulations in HK and JKis around 0.5 to 1◦C; however this small temperature changecorresponds to a substantially bigger increase in positive de-gree days and thus potential melt (Fig. 10).

Changes in precipitation show a more complex and un-certain response, with models showing regional variation in

the sign and magnitude of change despite an overall agree-ment of an increase in precipitation for the region as a whole.HadCM3 simulates the strongest increases in precipitation inthe western regions (HK, JK, KK) whilst ECHAM5 shows astrong increase in NB. Significantly, ECHAM5 gives a smalldecrease in JK and HK by the 2080s implying uncertaintyover the sign of change in this region. The two models de-viate from around the 2020s with HadCM3 showing a con-tinual increase in precipitation whilst the trend in ECHAM5reverses and declines. This reversal is linked to a jump inPDD (less so in mean temperature). This difference betweenthe two models is mainly linked to uncertainty in both themonsoon and WD simulated by the two models. However,increased precipitation doesn’t necessarily lead to increas-ing rates of accumulation. Changes in snowfall amounts area better indicator of possible changes in accumulation. Bythe 2080s the two simulations show an overall decrease insnowfall across the HKH with the greatest decreases around



33

836

837

838

Figure 7: Projected changes in regional climate by the 2080s relative to 1992-2008 from the 839

downscaled HadCM3 and ECHAM5 A1B simulations. Plots show change in annual average 840

temperature, degree days, precipitation and snowfall. 841

842

Figure 7. Projected changes in regional climate by the 2080s rel-ative to 1992–2008 from the downscaled HadCM3 and ECHAM5A1B simulations. Plots show change in annual average temperature,degree days, precipitation and snowfall.

the zero-degree isotherm. The HadCM3 simulations have anoverall increase in KK, HK and JK whilst ECHAM5 onlyhad an increase in the highest parts of KK. A study by Ri-dley et al. (2013) found that this could partly be explainedby changes in circulation with HadCM3 simulating increasedoccurrence of WD, with an associated overall 37 % increasein winter snowfall, whilst ECHAM5 showed no change inoccurrence of WD. Both models show a strong decrease insnowfall in the HP and NB regions despite an overall increasein precipitation. In HadCM3 the decrease in snowfall is con-centrated in the summer months, but ECHAM5 includes de-creases in winter snowfall.

5 Discussion

5.1 Climate drivers of future mass balance

The modelling performed here reveals a complex pattern ofclimatic change across the HKH corresponding to changesin circulation patterns and regional warming. All sub-regionshave an increase in potential ablation. The western part of the

HKH (HK, KK, JK) may have an increase in accumulation,although this is highly uncertain. In contrast, the eastern partof the HKH (HP, NB) shows a decrease in accumulation inboth ensemble members (Figs. 8 and 9) due in part to a sub-stantial reduction in the fraction of precipitation falling assnow, despite an overall increase in precipitation. However,it is possible that the model is over-estimating snowfall forthe present day and therefore over-estimating the magnitudeof decline in the future.

Taking these climate drivers as indicators of change in thecryosphere, plausible inferences can be made on the effecton future glacier mass balance, despite the models not fullyresolving glaciers. The glaciers in the HP and NB are mon-soon influenced and summer accumulating with winter ac-cumulation types dominating in KK and HK. The summersshow the largest increases in PDD despite the winters hav-ing a comparatively greater warming (Figs. 8 and 9). Theincrease in PDD in the HP and NB coincide with the timingof snowfall. Therefore, despite an intensification of the pre-cipitation in the east (NB, HP), less of the precipitation fallsas snow and thus accumulation decreases. These monsoon-affected glaciers may therefore be more vulnerable to warm-ing as any increase in ambient temperature is likely to have adouble effect: increasing potential melt as well as decreasingthe likelihood of precipitation falling as snow (Fujita, 2008a,b). This implies that glaciers in NB and HP may be more vul-nerable to warming than glaciers in other parts of the HKHfor similar levels of warming.

The glaciers in the NB and HP (in the absence of glacio-logical factors) may see greater reductions in their mass bal-ance than in HK, KK and JK. Despite ongoing warming,glaciers in the west may have increased accumulation seem-ingly dependent on changes in the frequency and intensity ofwestern disturbances although this is uncertain. This increasein snowfall by the 2080s in HadCM3 is offset by a large in-crease in possible ablation. In contrast, by the 2080s there isa decrease in snowfall simulated by ECHAM5.

The combined effect of increased potential ablation andsnowfall on mass balance can be estimated in terms of asimple degree day factor. A degree day factor is defined asthe melt per PDD. A simple degree day factor can be cal-culated for a zero change in mass balance over the 21stcentury for the KK region, such that changes in accumu-lation are balanced by melt. This can then be compared tothose derived from observations. The factor for the KK re-gion is 0.5 mm w.e. d−1 ◦C−1. This is an order of magni-tude smaller than calculated for the Dokriani glacier (Hock,2003; Singh et al., 2000) in HP which for clean snow is5.7 mm w.e. d−1 ◦C−1. It is correspondingly unlikely in thisscenario that increasing accumulation will offset increasingmelt in the HadCM3 scenario. Given this, and that presentmass balance is approximately neutral in the KK, it seemslikely, in these scenarios, that climate-driven changes in fu-ture mass balance will be negative. This is the case for thewhole HKH including the KK.



34

843

Figure 8: 30-year mean climatologies of air temperature, positive degree days, precipitation and 844

snowfall for the five sub-regions defined in this study from the downscaled HadCM3 A1B scenario. 845

Shown are 30-year climatologies for, 2020s (blue), 2050s (green) and 2080s (yellow) anomalised to 846

1992-2008. The regions are Hindu Kush (HK), Karakoram (KK), Jammu-Kashmir (JK), Himachal 847

Pradesh, Uttarakhand and West Nepal (HP) and East Nepal and Bhutan (NB). 848

849

Figure 8. Thirty-year mean climatologies of air temperature, PDDs, precipitation and snowfall for the five sub-regions defined in this studyfrom the downscaled HadCM3 A1B scenario. Shown are 30 yr climatologies for, 2020s (blue), 2050s (green) and 2080s (yellow) anomalisedto 1992–2008. The regions are Hindu Kush (HK), Karakoram (KK), Jammu–Kashmir (JK), Himachal Pradesh, Uttarakhand and West Nepal(HP) and East Nepal and Bhutan (NB).

The results presented here based on climate drivers ofchange imply a gradient from low to high rates of mass lossfrom west to east (KK to NB). This is in broad agreementwith observations of absolute changes in mass balance for thelast decade (Gardelle et al., 2013; Gardner et al., 2013; Kääbet al., 2012). However, the satellite studies find the greatestmass loss rates the JK which are not consistent in magnitudewith the climate drivers. This may be because either differentprocesses dominate in the future or perhaps due to the limita-tions of this study including the failure to consider the actualdistribution of glaciers at the present time and the absence ofglaciological response modelling to the climate drivers. Ofparticular note is the role of debris cover and glacier topogra-phy in altering the glaciological response to climatic change.Although debris-covered glaciers have been shown to havethinning rates comparable to debris-free glaciers (Nuimuraet al., 2012; Bolch et al., 2011) this may be in response todifferent drivers. This highlights the need for detailed glacier

response modelling to projected climate change to fully un-derstand the implications of climate change.

Within the projections presented here there is the largeamount of inter-annual variability seen in snowfall across theregion. The variability in accumulation and ablation as wellas the non-equilibrium state of glaciers means that the long-term mean glacier mass balance may be considerably dif-ferent from that derived from the short satellite record. TheECHAM5 simulation (Figs. 11 and 12) shows an increase insnowfall in HK and JK in the 2020s despite an overall de-cline by the 2080s. This is associated with a moderate degreeof warming, thus raising the counter-intuitive possibility thatfor small amounts of warming, snowfall may increase in thewestern part of the HKH. The aforementioned Karakoramanomaly and associated glacier thickening may therefore beconsistent with an overall warming trend in the region, al-though it should be noted that this requires further investiga-tion and is highly uncertain.



35

850

Figure 9: 30-year mean climatologies of air temperature, positive degree days, precipitation and 851

snowfall for the five sub-regions defined in this study from the downscaled ECHAM5 A1B scenario. 852

Shown are 30-year climatologies for the 2020s (blue), 2050s (green) and 2080s (yellow) anomalised 853

to 1992-2008. The regions are Hindu Kush (HK), Karakoram (KK), Jammu-Kashmir (JK), Himachal 854

Pradesh, Uttarakhand and West Nepal (HP) and East Nepal and Bhutan (NB). 855

856

Figure 9. Thirty-year mean climatologies of air temperature, PDDs, precipitation and snowfall for the five sub-regions defined in thisstudy from the downscaled ECHAM5 A1B scenario. Shown are 30 yr climatologies for the 2020s (blue), 2050s (green) and 2080s (yellow)anomalised to 1992–2008. The regions are Hindu Kush (HK), Karakoram (KK), Jammu–Kashmir (JK), Himachal Pradesh, Uttarakhand andWest Nepal (HP) and East Nepal and Bhutan (NB).

Some studies have found the rate of warming to increasewith elevation in the Himalayas and Tibetan plateau (e.g.Liu and Chen, 2000) stabilising around 5000 m a.s.l. (Qin etal., 2009). However, the magnitude and mechanisms drivingthis phenomenon remain unclear and uncertain (Kang et al.,2010). In the simulations analysed here the high mountains ingeneral warm faster than the Indo-Gangetic plain. This maybe due to snow–albedo feedbacks as seasonal and perennialsnow covers change but also changes in circulation patternsaffecting the advection of warm air into the mountains. How-ever, there is no consistent pattern of a correlation in warm-ing level and elevation above 2500 m a.s.l, but rather a strongseasonal uncertainty in the magnitude of projected warming.

5.2 Water resources

The overall impact of a declining future glacier mass balanceon future water resources is complicated by the glaciological

response, in which glaciers retreat to form new more stableequilibrium. The initial response may be increased wastageand thus an initial increase in runoff – the de-glaciation div-idend. However, this reduces as glaciers form new stablestates (Collins, 2008). A major driver of change in the east-ern HKH is the decline in snowfall; however, this is due to aswitch from snow to rain and is accompanied with an over-all increase in precipitation. This, combined with the lowseasonal storage of water in the snowpack (coincidence ofsummer accumulation and summer ablation) implies that re-newable water resources are likely to increase under climatechange. This is not necessarily the case in the western part ofthe HKH, which is a major source of runoff for the populatedIndus. The projections given here span a possible decreaseto increase in precipitation. In this case, the Indus might bemore sensitive to changes in glacier mass and extent thanother rivers of the region. This may be significant as the Indus



36

857

Figure 10: Projected change in annual mean air temperature, positive degree days precipitation and 858

snowfall from the HadCM3 (black) and ECHAM5 (red) downscaled A1B simulations. Data are 859

anomalies over the 21st century relative to 1992-2008. Data are smoothed with a 10-year moving 860

average. The regions are Hindu Kush (HK), Karakoram (KK), Jammu-Kashmir (JK), Himachal Pradesh, 861

Uttarakhand and West Nepal (HP) and East Nepal and Bhutan (NB). 862

863

864

Figure 10. Projected change in annual mean air temperature, PDDs, precipitation and snowfall from the HadCM3 (black) and ECHAM5(red) downscaled A1B simulations. Data are anomalies over the 21st century relative to 1992–2008. Data are smoothed with a 10 year movingaverage. The regions are Hindu Kush (HK), Karakoram (KK), Jammu–Kashmir (JK), Himachal Pradesh, Uttarakhand and West Nepal (HP)and East Nepal and Bhutan (NB).

population is thought to be vulnerable to change in water re-sources (Schaner et al., 2012).

5.3 Regional climate modelling limitations

It should be noted that there are a number of importantcaveats to the results presented here. Modelling the climatein areas of complex terrain, such as the HKH, is a difficulttask, particularly when making long-term climate projectionswhich require large amounts of computer power. In particu-lar, the ability of the RCM to capture snow/rain fractionationis likely to be poor, particularly given the large sub-grid vari-ation in topography. It has also been shown that the modelmay have a cold, wet bias potentially leading to an over-estimation in the present-day summer snowfall. In turn thismay implicate an overestimation of the degree to which ab-lation is projected to decrease. The RCM microphysics sim-

ulates the fall of snow and rain through the atmosphere witha conversion of snow to rain through sublimation and melt.The final snowfall is calculated for the grid box mean eleva-tion. Another factor in the ability of the model to capture thesnow/rain split in precipitation is the simulation of verticalmotion. Rasmussen et al. (2011) found in a coarser resolution(36 km) model that the strong vertical motion associated withgravity waves forming due to flow over topography were notcaptured as well as in the 6 km model. The strong up- anddown-drafts are associated with snow as opposed to rainfall,and result in less snowfall in a coarser model despite overallsimilar levels of precipitation (Rasmussen et al., 2011; Ikedaet al., 2010). The RCM used here employs parameterisationsof gravity waves and convection to address these unresolvedprocesses. It is unclear whether these parameterisations arelikely to increase or decrease snowfall compared to a modelexplicitly resolving these processes. However, what is clear



in the modelling applied here is that the relatively coarse res-olution of the RCM is unable to appropriately capture thevariation in topography. Furthermore, given computationallimitations, this study only considers two GCMs and as canbe seen in Fig. 4 the projected climate change in the Hi-malayas is uncertain on the sign of change in precipitationdespite an ability to appropriately simulate the present-daymonsoon. It is therefore the case that the two ensemble mem-bers given here do not fully sample the present-day range ofuncertainty in the large-scale circulation over the Indian sub-continent under climate change.

To better understand the possible impacts of climate onthe Himalayas requires improved observational data sets tobetter constrain model processes for the present day. In addi-tion, the latest generation of atmospheric models which areable to use a spatially variable grid resolution provide theopportunity to run simulations that are able to resolve to-pography, explicitly capturing convection and gravity waveformation, the distribution of glaciers and large-scale circu-lation changes associated with climate change. These toolsmay help both understand the past changes in glaciers andtheir associated drivers as well as better constrain future pro-jections (Molg and Kaser, 2011). Furthermore, developmentsof GCMs through higher resolution and improved processrepresentation may help reduce climate biases and lead toimproved projections of regional climate change. New high-resolution reanalysis products, such as the High Asia Re-analysis (Maussion et al., 2014) produced using numericalweather prediction techniques, can go some way to fillingthis data void.

6 Conclusion

In this study a pair of moderate-resolution regional climatesimulations is used to capture the implications of unmitigatedclimate change on the atmospheric environment of the HKHcryosphere. The implications of climate on accumulation andablation are complex and spatially variable and likely to beaffected by model resolution. To fully investigate these inter-actions requires high-resolution physically based modellingto capture the important role of orography in the region. Thelack of observational data limits the possible evaluation ofclimate model performance but this study finds that down-scaling meteorological reanalysis data provides a reasonableevaluation for the climate simulations. However, projectedchanges, particularly declines in future snowfall, are verysensitive to a possible high bias in present-day snowfall. Theresults presented here can be seen as a first step to assessingthe implications for regional climate. More detailed atmo-spheric and glacier modelling are required to make a morecomplete assessment.

Overall, using the model framework this study finds thatNB and HP Himalayas are likely to be more vulnerable tounmitigated climate change than glaciers in the Hindu Kush

and Karakoram. This is because, in the scenarios presentedhere, NB and HP glaciers accumulate mass during the sum-mer monsoon, which brings snowfall to the region, and arerelatively warmer in comparison to the HK and KK, whichhave a larger degree of winter accumulation. The effect ofwarming is twofold: first, to decrease the fraction of precipi-tation falling as snow and, second, to increase the rates of ab-lation. The magnitude of changes in snowfall under climatechange is highly uncertain. The increase in potential ablationis linked to both an increase in the melt period as well as itsintensity.

It is therefore projected that the eastern Himalayas willhave a stronger negative regional trend in mass balance thanthe central KK. However, the highest regions of mass loss inthe baseline observations were for the western Himalayas in-consistent with the future trends presented here (Kääb et al.,2012; Gardelle et al., 2013). However, other studies supporthigher values of mass loss in the eastern Himalayas (Gardneret al., 2013; Neckel et al., 2014). The results presented herelend support to these high rates of mass loss continuing intothe future.

The Western HKH shows possible increases in snowfalland thus mass accumulation under climate change partlydue to increased strength of the Western Disturbances inHadCM3. However, over the 21st century the warming trendand corresponding increased ablation is likely to dominateany increase in accumulation leading to reduced mass bal-ance. Despite this, glaciers in this region seem to be lessvulnerable to climate change than those in the east. There isconsiderably uncertainty in the regional detail and ECHAM5does not capture the increasing snowfall trend in the KK, in-stead showing a more complex response of an initial increasefor moderate warming with an overall decrease at higher lev-els of warming. The implication is that mitigation of climatechange at moderate levels may avoid some of the long-termprojected loss of glacier ice in the region. Climate variabil-ity, particularly in snowfall and precipitation is a large factorin the overall trends in precipitation and snowfall. Thus, ananalysis based on short-term observations may not fully cap-ture the signal of climate change.

The impact of climate change on renewable water re-sources is likely to be positive in the eastern HKH mainly dueto the increase in precipitation over the 21st century. The di-rect impact of climate change on water resources may there-fore be more important than the glaciological contribution.This is despite the projected greater impact on the glaciers ofthe eastern region from climate change. However, the west-ern HKH and the Indus will possibly see a decrease in pre-cipitation. Water sourced from glacier melt may therefore beof more importance to this highly populated catchment andthus will be more sensitive in terms of water resources to theprojected long-term glacial loss.



Acknowledgements.This work has been supported by the High-Noon project funded by the European Commission FrameworkProgramme 7 under Grant No. 227087. A. J. Wiltshire of theMet Office Hadley Centre was partly supported by the JointDECC/Defra Met Office Hadley Centre Climate Programme(GA01101). Jeff Ridley, Camilla Mathison and Jemma Gornalloffered useful comments during the preparation of this manuscript.Camilla Mathison set up and ran the RCM simulations. I wouldalso like to thank the reviewers and editor whose comments havesignificantly improved the paper.

Edited by: T. Bolch

References

Akhtar, M., Ahmad, N., and Booij, M. J.: The impact of cli-mate change on the water resources of Hindukush-Karakorum-Himalaya region under different glacier coverage scenarios, J.Hydrol., 355, 148–163, 2008.

Annamalai, H., Hamilton, K., and Sperber, K. R.: The SouthAsian summer monsoon and its relationship with ENSOin the IPCC AR4 simulations, J. Climate, 20, 1071–1092,doi:10.1175/Jcli4035.1, 2007.

Arendt, A., Bolch, T., Cogley, J. G., Gardner, A., Hagen, J.-O.,Hock, R., Kaser, G., Pfeffer, W. T., Moholdt, G., Paul, F., Radic,V., Andreassen, L., Bajracharya, S., Beedle, M., Berthier, E.,Bhambri, R., Bliss, A., Brown, I., Burgess, E., Burgess, D.,Cawkwell, F., Chinn, T., Copland, L., Davies, B., De Angelis,H., Dolgova, E., Filbert, K., Forester, R., Fountain, A., Frey, H.,Giffen, B., Glasser, N., Gurney, S., Hagg, W., Hall, D., Hari-tashya, U. K., Hartmann, G., Helm, C., Herreid, S., Howat, I.,Kapustin, G., Khromova, T., Kienholz, C., Koenig, M., Kohler,J., Kriegel, D., Kutuzov, S., Lavrentiev, I., LeBris, R., Lund, J.,Manley, W., Mayer, C., Miles, E., Li, X., Menounos, B., Mer-cer, A., Moelg, N., Mool, P., Nosenko, G., Negrete, A., Nuth,C., Pettersson, R., Racoviteanu, A., Ranzi, R., Rastner, P., Rau,F., Raup, B. H., Rich, J., Rott, H., Schneider, C., Seliverstov, Y.,Sharp, M., Sigurðsson, O., Stokes, C., Wheate, R., Winsvold, S.,Wolken, G., Wyatt, F., and Zheltyhinan N.: Randolph GlacierInventory [v2.0]: A Dataset of Global Glacier Outlines. GlobalLand Ice Measurements from Space, Boulder Colorado, USA,Digital Media, 2012.

Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impactsof a warming climate on water availability in snow-dominatedregions, Nature, 438, 303–309, 2005.

Bhutiyani, M. R.: Mass-balance studies on Siachen Glacier in theNubra valley, Karakoram Himalaya, India, J. Glaciol., 45, 112–118, 1999.

Bolch, T., Pieczonka, T., and Benn, D. I.: Multi-decadal mass lossof glaciers in the Everest area (Nepal Himalaya) derived fromstereo imagery, The Cryosphere, 5, 349–358, doi:10.5194/tc-5-349-2011, 2011.

Bolch, T., Kulkarni, A., Kääb, A., Huggel, C., Paul, F., Cogley, J.G., Frey, H., Kargel, J. S., Fujita, K., Scheel, M., Bajracharya,S., and Stoffel, M.: The State and Fate of Himalayan Glaciers,Science, 336, 310–314, 2012.

Bookhagen, B. and Burbank, D. W.: Topography, relief, andTRMM-derived rainfall variations along the Himalaya, Geophys.Res. Lett., 33, L08405, doi:10.1029/2006GL026037, 2006.

Bookhagen, B. and Burbank, D. W.: Toward a complete Himalayanhydrological budget: Spatiotemporal distribution of snowmeltand rainfall and their impact on river discharge, J. Geophys. Res.-Earth, 115, F03019, doi:10.1029/2009JF001426, 2010.

Christensen, J. H., Hewitson, B., Busuioc, A., Chen, A., Gao, X.,Held, I., Jones, R., Kolli, R. K., Kwon, W.-T., Laprise, R., Rueda,V. M., Mearns, L., Menéndez, C. G., Räisänen, J., Rinke, A.,Sarr, A., and Whetton, P.: Regional Climate Projections, in: Cli-mate Change 2007: The Physical Science Basis. Contribution ofWorking Group I to the Fourth Assessment Report of the Inter-governmental Panel on Climate Change, edited by: Solomon, S.,Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tig-nor, M., and Miller, H. L., Cambridge University Press, Cam-bridge, United Kingdom and New York, NY, USA, 2007.

Cogley, J. G.: A more complete version of the World Glacier Inven-tory, Ann. Glaciol., 50, 32–38, 2010.

Cogley, J. G., Kargel, J. S., Kaser, G., and Van der Veen, C. J.:Tracking the Source of Glacier Misinformation, Science, 327,522–522, 2010.

Collier, E., Mölg, T., Maussion, F., Scherer, D., Mayer, C., andBush, A. B. G.: High-resolution interactive modelling of themountain glacier–atmosphere interface: an application over theKarakoram, The Cryosphere, 7, 779–795, doi:10.5194/tc-7-779-2013, 2013.

Collins, D.: Climatic warming, glacier recession and runoff fromAlpine basins after the Little Ice Age maximum, Ann. Glaciol.,48, 119–124, 2008.

Copland, L., Sylvestre, T., Bishop, M. P., Shroder, J. F., Seong,Y. B., Owen, L. A., Bush, A., and Kamp, U.: Expanded andRecently Increased Glacier Surging in the Karakoram, Arct.Antarct. Alp. Res., 43, 503–516, 2011.

Cruz, R. V., Harasawa, H., Lal, M., Wu, S., Anokhin, Y., Punsalmaa,B., Honda, Y., Jafari, M., Li, C., and Ninh, N. H.: Asia, in: Cli-mate Change 2007: Impacts, Adaptation and Vulnerability. Con-tribution of Working Group II to the Fourth Assessment Reportof the Intergovernmental Panel on Climate Change, edited by:Parry, M. L., Canziani, O. F., Palutikof, J. P., Linden, P. J. v.d., and Hanson, C. E., Cambridge University Press, Cambridge,Cambridge, UK, 469–506, 2007.

Danielson, J. J. and Gesch, D. B.: Global multi-resolution terrain el-evation data 2010 (GMTED2010), US Geological Survey Open-File Report 2011–1073, p. 26, 2011.

Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli,P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G.,Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bid-lot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer,A. J., Haimberger, L., Healy, S. B., Hersbach, H., Holm, E. V.,Isaksen, L., Kallberg, P., Kohler, M., Matricardi, M., McNally,A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey,C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart, F.: TheERA-Interim reanalysis: configuration and performance of thedata assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597,2011.

Dimri, A. P., Yasunari, T., Wiltshire, A., Kumar, P., Mathison, C.,Ridley, J., and Jacob, D.: Application of regional climate modelsto the Indian winter monsoon over the western Himalayas, Sci.Total Environ., 468, S36–S47, 2013.


http://dx.doi.org/10.1175/Jcli4035.1

http://dx.doi.org/10.5194/tc-5-349-2011

http://dx.doi.org/10.5194/tc-5-349-2011

http://dx.doi.org/10.1029/2006GL026037

http://dx.doi.org/10.1029/2009JF001426

http://dx.doi.org/10.5194/tc-7-779-2013

http://dx.doi.org/10.5194/tc-7-779-2013


Essery, R. L. H., Best, M. J., Betts, R. A., Cox, P. M., and Taylor, C.M.: Explicit representation of subgrid heterogeneity in a GCMland surface scheme, J. Hydrometeorol., 4, 530–543, 2003.

Fowler, H. J. and Archer, D. R.: Conflicting signals of climaticchange in the Upper Indus Basin, J. Climate, 19, 4276–4293,2006.

Fujita, K.: Effect of precipitation seasonality on climatic sensitiv-ity of glacier mass balance, Earth Planet. Sc. Lett., 276, 14–19,2008a.

Fujita, K.: Influence of precipitation seasonality on glacier massbalance and its sensitivity to climate change, Ann. Glaciol., 48,88–92, 2008b.

Fujita, K. and Nuimura, T.: Spatially heterogeneous wastage of Hi-malayan glaciers, P. Natl. Acad. Sci. USA, 108, 14011–14014,2011.

Gardelle, J., Berthier, E., and Arnaud, Y.: Slight mass gainof Karakoram glaciers in the early twenty-first century, Nat.Geosci., 5, 322–325, 2012.

Gardelle, J., Berthier, E., Arnaud, Y., and Kääb, A.: Region-wideglacier mass balances over the Pamir-Karakoram-Himalaya dur-ing 1999–2011, The Cryosphere, 7, 1263–1286, doi:10.5194/tc-7-1263-2013, 2013.

Gardner, A. S., Moholdt, G., Cogley, J. G., Wouters, B., Arendt,A. A., Wahr, J., Berthier, E., Hock, R., Pfeffer, W. T., Kaser,G., Ligtenberg, S. R. M., Bolch, T., Sharp, M. J., Hagen, J. O.,van den Broeke, M. R., and Paul, F.: A Reconciled Estimate ofGlacier Contributions to Sea Level Rise: 2003 to 2009, Science,340, 852–857, 2013.

GLIMS: Global Land Ice Measurements from Space glacierdatabase, updated 2013. Compiled and made available by the in-ternational GLIMS community and the National Snow and IceData Center, Boulder CO, USA, 2005.

Gordon, C., Cooper, C., Senior, C. A., Banks, H., Gregory, J. M.,Johns, T. C., Mitchell, J. F. B., and Wood, R. A.: The simulationof SST, sea ice extents and ocean heat transports in a versionof the Hadley Centre coupled model without flux adjustments,Clim. Dynam., 16, 147–168, 2000.

Hewitt, K.: The Karakoram anomaly? Glacier expansion and the’elevation effect,’ Karakoram Himalaya, Mt Res Dev, 25, 332–340, 2005.

Hewitt, K.: Glacier Change, Concentration, and Elevation Effects inthe Karakoram Himalaya, Upper Indus Basin, Mt. Res. Dev., 31,188-200, 2011.

Hock, R.: Temperature index melt modelling in mountain areas, J.Hydrol., 282, 104–115, 2003.

Hock, R.: Glacier melt: a review of processes and their modelling,Prog. Phys. Geog., 29, 362–391, 2005.

Ikeda, K., Rasmussen, R., Liu, C. H., Gochis, D., Yates, D., Chen,F., Tewari, M., Barlage, M., Dudhia, J., Miller, K., Arsenault, K.,Grubisic, V., Thompson, G., and Guttman, E.: Simulation of sea-sonal snowfall over Colorado, Atmos. Res., 97, 462–477, 2010.

Immerzeel, W. W., van Beek, L. P. H., and Bierkens, M. F. P.: Cli-mate Change Will Affect the Asian Water Towers, Science, 328,1382–1385, 2010.

IPCC: Climate Change 2007: The Physical Science Basis. Con-tribution of Working Group I to the Fourth Assessment Reportof the Intergovernmental Panel on Climate Change, edited by:Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Av-

eryt, K. B., Tignor, M., and Miller, H. L., Cambridge UniversityPress, Cambridge, UK and New York, NY, USA, 2007.

Jacob, T., Wahr, J., Pfeffer, W. T., and Swenson, S.: Recent con-tributions of glaciers and ice caps to sea level rise, Nature, 482,514–518, 2012.

Jeelani, G., Feddema, J. J., van der Veen, C. J., and Stearns, L.: Roleof snow and glacier melt in controlling river hydrology in Liddarwatershed (western Himalaya) under current and future climate,Water Resour. Res., 48, W12508, doi:10.1029/2011WR011590,2012.

Kääb, A., Berthier, E., Nuth, C., Gardelle, J., and Arnaud, Y.:Contrasting patterns of early twenty-first-century glacier masschange in the Himalayas, Nature, 488, 495–498, 2012.

Kang, I. S., Jin, K., Wang, B., Lau, K. M., Shukla, J., Krishna-murthy, V., Schubert, S. D., Wailser, D. E., Stern, W. F., Kitoh,A., Meehl, G. A., Kanamitsu, M., Galin, V. Y., Satyan, V., Park,C. K., and Liu, Y.: Intercomparison of the climatological varia-tions of Asian summer monsoon precipitation simulated by 10GCMs, Clim. Dynam., 19, 383–395, 2002.

Kang, S. C., Xu, Y. W., You, Q. L., Flugel, W. A., Pepin, N., andYao, T. D.: Review of climate and cryospheric change in the Ti-betan Plateau, Environ. Res. Lett., 5, 015101, doi:10.1088/1748-9326/5/1/015101, 2010.

Kaser, G., Grosshauser, M., and Marzeion, B.: Contribution poten-tial of glaciers to water availability in different climate regimes,P. Natl. Acad. Sci. USA, 107, 20223–20227, 2010.

Kripalani, R. H., Oh, J. H., Kulkarni, A., Sabade, S. S., and Chaud-hari, H. S.: South Asian summer monsoon precipitation variabil-ity: Coupled climate model simulations and projections underIPCC AR4, Theor. Appl. Climatol., 90, 133–159, 2007.

Kumar, K. R., Sahai, A. K., Kumar, K. K., Patwardhan, S. K.,Mishra, P. K., Revadekar, J. V., Kamala, K., and Pant, G. B.:High-resolution climate change scenarios for India for the 21stcentury, Curr. Sci., 90, 334–345, 2006.

Kumar, P., Wiltshire, A., Mathison, C., Asharaf, S., Ahrens, B.,Lucas-Picher, P., Christensen, J. H., Gobiet, A., Saeed, F., Hage-mann, S., and Jacob, D.: Downscaled climate change projectionswith uncertainty assessment over India using a high resolutionmulti-model approach, Sci. Total Environ., 468, S18–S30, 2013.

Liu, X. D. and Chen, B. D.: Climatic warming in the Tibetan Plateauduring recent decades, Int. J. Climatol., 20, 1729–1742, 2000.

Lucas-Picher, P., Christensen, J. H., Saeed, F., Kumar, P., Asharaf,S., Ahrens, B., Wiltshire, A., Jacob, D., and Hagemann, S.: Canregional climate models represent the Indian monsoon?, J. Hy-drometeorol., 12, 849–868, doi:10.1175/2011jhm1327.1, 2011.

Mathison, C., Wiltshire, A. J., Dimri, A. P., Falloon, P., Jacob, D.,Kumar, P., Moors, E., Ridley, J., Siderius, C., Stoffel, M., andYasunari, T.: Regional Projections of North Indian Climate forAdaptation Studies, Sci. Total Environ., 468, S4–S17. 2013.

Maussion, F., Scherer, D., Finkelnburg, R., Richters, J., Yang, W.,and Yao, T.: WRF simulation of a precipitation event over theTibetan Plateau, China – an assessment using remote sensing andground observations, Hydrol. Earth Syst. Sci., 15, 1795–1817,doi:10.5194/hess-15-1795-2011, 2011.

Maussion, F., Scherer, D., Mölg, T., Collier, E., Curio, J., andFinkelnburg, R.: Precipitation Seasonality and Variability overthe Tibetan Plateau as Resolved by the High Asia Reanalysis*, J.Climate, 27, 1910–1927, doi:10.1175/JCLI-D-13-00282.1, 2014.


http://dx.doi.org/10.5194/tc-7-1263-2013

http://dx.doi.org/10.5194/tc-7-1263-2013

http://dx.doi.org/10.1029/2011WR011590

http://dx.doi.org/10.1088/1748-9326/5/1/015101

http://dx.doi.org/10.1088/1748-9326/5/1/015101

http://dx.doi.org/10.1175/2011jhm1327.1

http://dx.doi.org/10.5194/hess-15-1795-2011

http://dx.doi.org/10.1175/JCLI-D-13-00282.1


Meehl, G. A., Stocker, T. F., Collins, W. D., Friedlingstein, P., Gaye,A. T., Gregory, J. M., Kitoh, A., Knutti, R., Murphy, J. M., Noda,A., Raper, S. C. B., Watterson, I. G., Weaver, A. J., and Zhao, Z.-C.: Global Climate Projections, in: The Physical Science Basis.Contribution of Working Group I to the Fourth Assessment Re-port of the Intergovernmental Panel on Climate Change, editedby: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M.,Averyt, K. B., Tignor, M., and Miller, H. L., Cambridge Univer-sity Press, Cambridge, UK and New York, NY, USA, 2007.

Molg, T. and Kaser, G.: A new approach to resolvingclimate-cryosphere relations: Downscaling climate dynamicsto glacier-scale mass and energy balance without statisti-cal scale linking, J. Geophys. Res.-Atmos., 116, D16101,doi:10.1029/2011JD015669, 2011.

Moors, E. J., Groot, A., Biemans, H., van Scheltinga, C. T., Siderius,C., Stoffel, M., Huggel, C., Wiltshire, A., Mathison, C., Ridley,J., Jacob, D., Kumar, P., Bhadwal, S., Gosain, A., and Collins, D.N.: Adaptation to changing water resources in the Ganges basin,northern India, Environmental Science & Policy, 14, 758–769, doi:10.1016/j.envsci.2011.03.005, 2011.

Nakicenovic, N., Alcamo, J., Davis, G., de Vries, B., Fenhann, J.,Gaffin, S., Gregory, K., Grübler, A., Jung, T. Y., Kram, T., LaRovere, E. L., Michaelis, L., Mori, S., Morita, T., Pepper, W.,Pitcher, H., Price, L., Riahi, K., Roehr, l. A., Rogner, H.-H.,Sankovski, A., Schlesinger, M., Shukla, P., Smith, S., Swart, R.,van Rooijen, S., Nadejda, V., and Dadi, Z.: IPCC Special reporton emissions scenarios, Cambridge, UK, 2006.

Neckel, N., Kropácek, J., Bolch, T., and Hochschild, V.: Glaciermass changes on the Tibetan Plateau 2003–2009 derived fromICESat laser altimetry measurements, Environ. Res. Lett., 9,014009, doi:10.1088/1748-9326/9/1/014009, 2014.

Nuimura, T., Fujita, K., Yamaguchi, S., and Sharma, R. R.: Eleva-tion changes of glaciers revealed by multitemporal digital ele-vation models calibrated by GPS survey in the Khumbu region,Nepal Himalaya, 1992–2008, J. Glaciol., 58, 648–656, 2012.

Ohmura, A.: Physical basis for the temperature-based melt-indexmethod, J. Appl. Meteorol., 40, 753–761, 2001.

Pope, V. D., Gallani, M. L., Rowntree, P. R., and Stratton, R. A.: Theimpact of new physical parametrizations in the Hadley Centreclimate model: HadAM3, Clim. Dynam., 16, 123–146, 2000.

Putkonen, J. K.: Continuous snow and rain data at 500 to 4400 maltitude near Annapurna, Nepal, 1999–2001, Arct. Antarct. Alp.Res., 36, 244–248, 2004.

Qin, J., Yang, K., Liang, S. L., and Guo, X. F.: The altitudinal depen-dence of recent rapid warming over the Tibetan Plateau, Clim.Change, 97, 321–327, 2009.

Rabatel, A., Francou, B., Soruco, A., Gomez, J., Cáceres, B., Ce-ballos, J. L., Basantes, R., Vuille, M., Sicart, J.-E., Huggel, C.,Scheel, M., Lejeune, Y., Arnaud, Y., Collet, M., Condom, T.,Consoli, G., Favier, V., Jomelli, V., Galarraga, R., Ginot, P., Mais-incho, L., Mendoza, J., Ménégoz, M., Ramirez, E., Ribstein,P., Suarez, W., Villacis, M., and Wagnon, P.: Current state ofglaciers in the tropical Andes: a multi-century perspective onglacier evolution and climate change, The Cryosphere, 7, 81–102, doi:10.5194/tc-7-81-2013, 2013.

Radic, V. and Hock, R.: Regional and global volumes of glaciersderived from statistical upscaling of glacier inventory data, J.Geophys. Res.-Earth, 115, F01010, doi:10.1029/2009JF001373,2010.

Rajeevan, M., Bhate, J., and Jaswal, A. K.: Analysis of variabilityand trends of extreme rainfall events over India using 104 yearsof gridded daily rainfall data, Geophys. Res. Lett., 35, L18707,doi:10.1029/2008GL035143, 2008.

Rasmussen, R., Liu, C. H., Ikeda, K., Gochis, D., Yates, D., Chen,F., Tewari, M., Barlage, M., Dudhia, J., Yu, W., Miller, K., Arse-nault, K., Grubisic, V., Thompson, G., and Gutmann, E.: High-Resolution Coupled Climate Runoff Simulations of SeasonalSnowfall over Colorado: A Process Study of Current and WarmerClimate, J. Climate, 24, 3015–3048, 2011.

Raup, B., Racoviteanu, A., Khalsa, S. J. S., Helm, C., Armstrong,R., and Arnaud, Y.: The GLIMS geospatial glacier database: Anew tool for studying glacier change, Global Planet. Change, 56,101–110, 2007.

Ridley, J., Wiltshire, A., and Mathison, C.: More frequent occur-rence of westerly disturbances in Karakoram up to 2100, Sci.Total Environ., 468, S31–S35, 2013.

Roeckner, E., Bäuml, G., Bonaventura, L., Brokopf, R., Esch,M., Giorgetta, M., Hagemann, S., Kirchner, I., Kornblueh, L.,Manzini, E., Rhodin, A., Schlese, U., Schulzweida, U., andTompkins, A.: The Atmospheric Circulation model ECHAM 5Part 1: Model description, Max Plank Institute for MeteorologyReport 349, 2003.

Schaner, N., Voisin, N., Nijssen, B., and Lettenmaier, D. P.: Thecontribution of glacier melt to streamflow, Environ. Res. Lett., 7,034029, doi:10.1088/1748-9326/7/3/034029, 2012.

Scherler, D., Bookhagen, B., and Strecker, M. R.: Spatially variableresponse of Himalayan glaciers to climate change affected bydebris cover, Nat. Geosci., 4, 156–159, 2011.

Siderius, C., Biemans, H., Wiltshire, A., Rao, S., Franssen, W. H.P., Kumar, P., Gosain, A. K., van Vliet, M. T. H., and Collins,D. N.: Snowmelt contributions to discharge of the Ganges, Sci.Total Environ., 468, S93–S101, 2013.

Singh, P., Kumar, N., and Arora, M.: Degree-day factors for snowand ice for Dokriani Glacier, Garhwal Himalayas, J. Hydrol.,235, 1–11, 2000.

Turner, A. G., Inness, P. M., and Slingo, J. M.: The role of the ba-sic state in the ENSO-monsoon relationship and implications forpredictability, Q. J. Roy. Meteor. Soc., 131, 781–804, 2005.

Turner, A. G. and Annamalai, H.: Climate change and the SouthAsian summer monsoon, Nat. Clim. Change, 2, 587–595, 2012.

US Navy: Fleet Numerical Meteorology and Oceanography Cen-ter/U.S. Navy/U. S. Department of Defense U.S. Navy 10-Minute Global Elevation and Geographic Characteristics, in, Re-search Data Archive at the National Center for AtmosphericResearch, Computational and Information Systems Laboratory,Boulder, Colorado, 1980.

Ueda, H., Iwai, A., Kuwako, K., and Hori, M. E.: Impact of an-thropogenic forcing on the Asian summer monsoon as sim-ulated by eight GCMs, Geophys. Res. Lett., 33, L06703,doi:10.1029/2005GL025336, 2006.

Viviroli, D., Durr, H. H., Messerli, B., Meybeck, M., and Wein-gartner, R.: Mountains of the world, water towers for humanity:Typology, mapping, and global significance, Water Resour. Res.,43, W07447, doi:10.1029/2006WR005653, 2007.

WWF: An Overview of Glaciers, Glacier Retreat, and SubsequentImpacts in Nepal, India and China, WWF Nepal Program, Kath-mandu, 2005.


http://dx.doi.org/10.1029/2011JD015669

http://dx.doi.org/10.1016/j.envsci.2011.03.005

http://dx.doi.org/10.1088/1748-9326/9/1/014009

http://dx.doi.org/10.5194/tc-7-81-2013

http://dx.doi.org/10.1029/2009JF001373

http://dx.doi.org/10.1029/2008GL035143

http://dx.doi.org/10.1088/1748-9326/7/3/034029

http://dx.doi.org/10.1029/2005GL025336

http://dx.doi.org/10.1029/2006WR005653


Xu, B. Q., Cao, J. J., Hansen, J., Yao, T. D., Joswia, D. R., Wang,N. L., Wu, G. J., Wang, M., Zhao, H. B., Yang, W., Liu, X. Q.,and He, J. Q.: Black soot and the survival of Tibetan glaciers, P.Natl. Acad. Sci. USA, 106, 22114–22118, 2009.

Yatagai, A. and Yasunari, T.: Trends and Decadal-Scale Fluctua-tions of Surface Air-Temperature and Precipitation over Chinaand Mongolia during the Recent 40-Year Period (1951–1990), J.Meteorol. Soc. Jpn, 72, 937–957, 1994.

Yatagai, A., Arakawa, O., Kamiguchi, K., Kawamoto, H., Nodzu,M. I., and Hamada, A.: A 44-Year Daily Gridded PrecipitationDataset for Asia Based on a Dense Network of Rain Gauges,Sola, 5, 137–140, 2009.


Documents

Climate change implications for the glaciers of the Hindu Kush, … · 2020. 6. 23. · data (Kääb et al., 2012) revealed a complex pattern in mass balance (Table 1), with the Karakoram